Electrical Power Subsystem for the Euclid Spacecraft

European Space Agency in the frame of Cosmic Vision 2015-2025 program [ 1 ]. It is a cosmology mission whose prime objective is to study the geometry and the nature of the dark matter and the dark energy with unprecedented accuracy. The spacecraft will be launched in 2020 by a Soyuz launcher, to perform a six-year survey of the extragalactic sky from a large-amplitude orbit around Lagrange point L2 of the Sun-Earth system. This paper outlines the Euclid Electrical Power Subsystem (EPS) design, providing a description of the major design drivers and resulting configuration, with a view to highlight aspects that could be considered for future designs

Editorial: Non-invasive brain stimulation techniques in neurological and neuropsychiatric disorders: Physiological and molecular evidence

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Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

Objective: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). Methods: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. Results: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. Conclusions: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection

An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a significant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai

A Comprehensive Genetic Study of Classical Hodgkin Lymphoma Using Circulating Tumor DNA

: This study analyzed the genetics of classical Hodgkin lymphoma (cHL) by using circulating tumor DNA (ctDNA). Two genetic subtypes were identified, differing in genetic instability mechanisms: one subtype (64% of cases) showed a higher mutation load and a higher fraction of mutations associated with activation-induced cytidine deaminase and microsatellite instability signatures, while the other subtype (36% of cases) exhibited chromosomal instability with more somatic copy number alterations. Whole-genome duplication was more common in cHL compared to other B-cell tumors and emerged as a prognostic biomarker for patients undergoing ABVD-based therapy. Non-coding regulatory mutations, similar to those in diffuse large B-cell lymphoma, were highly prevalent in 86% of cHL. A recurrent somatic expression quantitative trait locus (seQTL) involving the BCL6 gene was found in 30% of cases. The seQTL of BCL6 aligned with accessible chromatin and increased H3K27 acetylation in cHL, disrupted PRDM1 binding, and co-occurred with BCL6 expression in cHL cells. Weak to strong expression of BCL6 was observed in 68% of cases and BCL6 expression associated with gene repression similarly in cHL and germinal center B cells. After BCL6 degradation, the core set of genes directly bound and regulated by BCL6 was derepressed in cHL and proliferation was impaired. The number and clonality of neoantigens was associated with tumor microenvironment type and response to checkpoint blockade. Finally, ctDNA analysis was suggested as a tool to distinguish ambiguous PET/CT-positive lesions post-treatment